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Thomas Jefferson High School for Science and Technology Hallway Traffic Simulation

Create an accurate simulation of Thomas Jefferson High School for Science and Technology hallways and traffic, analyze results, and improve movement.

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Thomas Jefferson High School for Science and Technology Hallway Traffic Simulation

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  1. Thomas Jefferson High School for Science and Technology Hallway Traffic Simulation By: Paul Woods

  2. PURPOSE • Create an accurate simulation of TJHSST hallways and traffic • Visualize the simulation to increase accessibility and reach • Analyze results of the simulation and modify conditions to ease congestion and improve movement

  3. AREA TO BE MAPPED

  4. AREA TO BE MAPPED

  5. BACKROUND RESEARCH • Continuum Crowds by Adrian Trueille, Seth Cooper, Zoran Popovic—University of Washington, Electronic Arts • Calculated movement and collision at the same time • Saved significant processing time • Finding Multi-Constrained Feasible Paths By Using Depth-First Search by Zhenjiang L and J.J. Garcia-Luna-Aceves • Provides a model algorithm for finding paths from one place to another

  6. BACKROUND RESEARCH • Continuum Performance OpenGL: Platform Independence Techniques • by Tom True, Brad Grantham, Bob Kuehne, and Dave Shreiner. • Errors and performance Interactive Graphics Using OpenGL and the Graphix Windowing Toolkit by Rance D. Necaise, Computer Science Department Washington and Lee University • Graphix Windowing Toolkit

  7. SIMILAR PAST RESEARCH • Traffic Dynamics in Scholastic Environments by Alex Kotkova

  8. EXPECTED RESULTS • Accurate data involving hallway movement • A working model allowing possible changes to be simulated

  9. CURRENT MODELS • Made using C programming language • Simulates small model of school and shrunken student body • Organizes data in arrays and hashes • Effectively returns necessary and expected output • Will be expanded later to include entire school and student body

  10. TOOLS IN CURRENT MODEL • Organizational structures such as hashes to hold hall, room and student data • An improved moving search to determine student paths of movement

  11. MODEL SCHOOL MAP

  12. Example Output From The Program: StudentName Start 10:08 10:11 Finish Student0 Area3 Area5 Area2 Area2 Student1 Area1 Area4 Area5 Area3 Student2 Area1 Area4 Area5 Area3 Student3 Area2 Area5 Area4 Area0 Student4 Area1 Area4 Area5 Area2 Student5 Area3 Area5 Area2 Area2 ...

  13. Students: jon sally hill karel jim greg megan joe sarah ROOMS: RoomNumber: RoomName Hall0 Hall1 Hall2 Hall3 Hall4 0 0 0 0 0 0 1 r1230 2 3 0 0 0 2 r1240 1 0 0 0 0 3 r1250 4 2 0 0 0 HALLS: HallNumber: Room0 Room1 Room2 Room3 Room4 Room5 Room6 0 1 3 0 0 0 0 0 1 2 0 0 0 0 0 0 2 1 3 0 0 0 0 0 3 1 0 0 0 0 0 0 4 3 0 0 0 0 0 0 SCHEDULES: StudentNum Name Event0 Event1 Event2 Event3 0 0 0 0 0 1 jon 0 1 2 1 2 sally 0 2 3 2 3 hill 0 2 1 1 4 karel 0 2 3 2 5 jim 0 3 2 3 6 greg 0 2 1 1 7 megan 0 2 2 3 8 joe 0 3 1 1 9 sarah 0 3 3 3 HallNum hall0 hall1 hall2 hall3 Hall0: 0 0 0 0 Hall1: 2 4 0 0 Hall2: 1 3 0 0 Hall3: 2 0 0 0 Hall4: 1 0 0 0 Time-Events Array Event Time 0 0 1 905 2 915 3 0

  14. RESULTS TABLE Traffic as a Percentage of Students Using Hallway Time Hall

  15. POSSIBILITIES AS TO WHY THIS OCCURED IN THIS EXPERIMENT • More students going from rooms 0-1 to 2-3 than from 2-3 to 0-1 • Class sizes of classes in rooms 2-3 bigger than in rooms 1-0 • Students entering rooms 0-1 were closer and thus able to get to their classes more quickly • Students do not take into account traffic when determining movement

  16. POSSIBLE SOLUTIONS • Switch classes in rooms 1 and 2 to see how that affects traffic. • Program students avoid congested areas

  17. FUTURE GOALS • Finalize second phase algorithm • Expand original model to include entire school and student body • Take into account congestion when determining student movement • Add visualization using OpenGL

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